knitr::opts_chunk$set(warning = F, message = F)

Reading & Saving the Data

library(tidyverse)

years <- 2017:2020
quarters <- 1:4
types <- c("Electric", "Gas")
months <- 1:42
pge_elec_gas <- NULL

for (quarter in quarters){
  for (year in years){
    for (type in types){
       if ((quarter %in% 3:4) & (year %in% 2020)){
        next 
       }
      
      filename <- 
        paste0(
          "PGE_",
          year, 
          "_Q", 
          quarter,
          "_",
          type,
          "UsageByZip.csv"
        )
     
      print(filename)
      
      
      temp <- read_csv(filename)
      
      if(type %in% "Gas"){
        print(temp)
        temp <- temp %>% 
          mutate(TOTALKBTU = 100 * TOTALTHM) %>% 
          select(-TOTALTHM, -AVERAGETHM)
      } else {
        temp <- temp %>% 
        mutate(TOTALKBTU = 3.412 * TOTALKWH) %>% 
          select(-TOTALKWH, -AVERAGEKWH)
      }
      
      pge_elec_gas <- rbind(pge_elec_gas, temp)

    }
  }
}
## [1] "PGE_2017_Q1_ElectricUsageByZip.csv"
## [1] "PGE_2017_Q1_GasUsageByZip.csv"
## # A tibble: 3,486 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     1  2017 Gas- Commerc~ Y                     0        0         NA
##  2   92304     2  2017 Gas- Commerc~ Y                     0        0         NA
##  3   92304     3  2017 Gas- Commerc~ Y                     0        0         NA
##  4   92365     1  2017 Gas- Commerc~ Y                     0        0         NA
##  5   92365     2  2017 Gas- Commerc~ Y                     0        0         NA
##  6   92365     3  2017 Gas- Commerc~ Y                     0        0         NA
##  7   93203     1  2017 Gas- Commerc~ Y                     0        0         NA
##  8   93203     2  2017 Gas- Commerc~ Y                     0        0         NA
##  9   93203     3  2017 Gas- Commerc~ Y                     0        0         NA
## 10   93204     1  2017 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,476 more rows
## [1] "PGE_2018_Q1_ElectricUsageByZip.csv"
## [1] "PGE_2018_Q1_GasUsageByZip.csv"
## # A tibble: 3,499 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1      NA    NA    NA Gas-          <NA>                 NA       NA         NA
##  2   92304     1  2018 Gas- Commerc~ Y                     0        0         NA
##  3   92304     2  2018 Gas- Commerc~ Y                     0        0         NA
##  4   92304     3  2018 Gas- Commerc~ Y                     0        0         NA
##  5   92365     1  2018 Gas- Commerc~ Y                     0        0         NA
##  6   92365     2  2018 Gas- Commerc~ Y                     0        0         NA
##  7   92365     3  2018 Gas- Commerc~ Y                     0        0         NA
##  8   93203     1  2018 Gas- Commerc~ Y                     0        0         NA
##  9   93203     2  2018 Gas- Commerc~ Y                     0        0         NA
## 10   93203     3  2018 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,489 more rows
## [1] "PGE_2019_Q1_ElectricUsageByZip.csv"
## [1] "PGE_2019_Q1_GasUsageByZip.csv"
## # A tibble: 3,499 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     1  2019 Gas- Commerc~ Y                     0        0         NA
##  2   92304     2  2019 Gas- Commerc~ Y                     0        0         NA
##  3   92304     3  2019 Gas- Commerc~ Y                     0        0         NA
##  4   92365     1  2019 Gas- Commerc~ Y                     0        0         NA
##  5   92365     2  2019 Gas- Commerc~ Y                     0        0         NA
##  6   92365     3  2019 Gas- Commerc~ Y                     0        0         NA
##  7   93203     1  2019 Gas- Commerc~ Y                     0        0         NA
##  8   93203     2  2019 Gas- Commerc~ Y                     0        0         NA
##  9   93203     3  2019 Gas- Commerc~ Y                     0        0         NA
## 10   93204     1  2019 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,489 more rows
## [1] "PGE_2020_Q1_ElectricUsageByZip.csv"
## [1] "PGE_2020_Q1_GasUsageByZip.csv"
## # A tibble: 3,493 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     1  2020 Gas- Commerc~ Y                     0        0         NA
##  2   92304     2  2020 Gas- Commerc~ Y                     0        0         NA
##  3   92304     3  2020 Gas- Commerc~ Y                     0        0         NA
##  4   92365     1  2020 Gas- Commerc~ Y                     0        0         NA
##  5   92365     2  2020 Gas- Commerc~ Y                     0        0         NA
##  6   92365     3  2020 Gas- Commerc~ Y                     0        0         NA
##  7   93203     1  2020 Gas- Commerc~ Y                     0        0         NA
##  8   93203     2  2020 Gas- Commerc~ Y                     0        0         NA
##  9   93203     3  2020 Gas- Commerc~ Y                     0        0         NA
## 10   93204     1  2020 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,483 more rows
## [1] "PGE_2017_Q2_ElectricUsageByZip.csv"
## [1] "PGE_2017_Q2_GasUsageByZip.csv"
## # A tibble: 3,489 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     4  2017 Gas- Commerc~ Y                     0        0         NA
##  2   92304     5  2017 Gas- Commerc~ Y                     0        0         NA
##  3   92304     6  2017 Gas- Commerc~ Y                     0        0         NA
##  4   92365     4  2017 Gas- Commerc~ Y                     0        0         NA
##  5   92365     5  2017 Gas- Commerc~ Y                     0        0         NA
##  6   92365     6  2017 Gas- Commerc~ Y                     0        0         NA
##  7   93203     4  2017 Gas- Commerc~ Y                     0        0         NA
##  8   93203     5  2017 Gas- Commerc~ Y                     0        0         NA
##  9   93203     6  2017 Gas- Commerc~ Y                     0        0         NA
## 10   93204     4  2017 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,479 more rows
## [1] "PGE_2018_Q2_ElectricUsageByZip.csv"
## [1] "PGE_2018_Q2_GasUsageByZip.csv"
## # A tibble: 3,502 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1      NA    NA    NA Gas-          <NA>                 NA       NA         NA
##  2   92304     4  2018 Gas- Commerc~ Y                     0        0         NA
##  3   92304     5  2018 Gas- Commerc~ Y                     0        0         NA
##  4   92304     6  2018 Gas- Commerc~ Y                     0        0         NA
##  5   92365     4  2018 Gas- Commerc~ Y                     0        0         NA
##  6   92365     5  2018 Gas- Commerc~ Y                     0        0         NA
##  7   92365     6  2018 Gas- Commerc~ Y                     0        0         NA
##  8   93203     4  2018 Gas- Commerc~ Y                     0        0         NA
##  9   93203     5  2018 Gas- Commerc~ Y                     0        0         NA
## 10   93203     6  2018 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,492 more rows
## [1] "PGE_2019_Q2_ElectricUsageByZip.csv"
## [1] "PGE_2019_Q2_GasUsageByZip.csv"
## # A tibble: 3,499 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1      NA    NA    NA Gas-          <NA>                 NA       NA         NA
##  2   92304     4  2019 Gas- Commerc~ Y                     0        0         NA
##  3   92304     5  2019 Gas- Commerc~ Y                     0        0         NA
##  4   92304     6  2019 Gas- Commerc~ Y                     0        0         NA
##  5   92365     4  2019 Gas- Commerc~ Y                     0        0         NA
##  6   92365     5  2019 Gas- Commerc~ Y                     0        0         NA
##  7   92365     6  2019 Gas- Commerc~ Y                     0        0         NA
##  8   93203     4  2019 Gas- Commerc~ Y                     0        0         NA
##  9   93203     5  2019 Gas- Commerc~ Y                     0        0         NA
## 10   93203     6  2019 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,489 more rows
## [1] "PGE_2020_Q2_ElectricUsageByZip.csv"
## [1] "PGE_2020_Q2_GasUsageByZip.csv"
## # A tibble: 3,461 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     4  2020 Gas- Commerc~ Y                     0        0         NA
##  2   92304     5  2020 Gas- Commerc~ Y                     0        0         NA
##  3   92304     6  2020 Gas- Commerc~ Y                     0        0         NA
##  4   92365     4  2020 Gas- Commerc~ Y                     0        0         NA
##  5   92365     5  2020 Gas- Commerc~ Y                     0        0         NA
##  6   92365     6  2020 Gas- Commerc~ Y                     0        0         NA
##  7   93203     4  2020 Gas- Commerc~ Y                     0        0         NA
##  8   93203     5  2020 Gas- Commerc~ Y                     0        0         NA
##  9   93203     6  2020 Gas- Commerc~ Y                     0        0         NA
## 10   93204     4  2020 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,451 more rows
## [1] "PGE_2017_Q3_ElectricUsageByZip.csv"
## [1] "PGE_2017_Q3_GasUsageByZip.csv"
## # A tibble: 3,491 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     7  2017 Gas- Commerc~ Y                     0        0         NA
##  2   92304     8  2017 Gas- Commerc~ Y                     0        0         NA
##  3   92304     9  2017 Gas- Commerc~ Y                     0        0         NA
##  4   92365     7  2017 Gas- Commerc~ Y                     0        0         NA
##  5   92365     8  2017 Gas- Commerc~ Y                     0        0         NA
##  6   92365     9  2017 Gas- Commerc~ Y                     0        0         NA
##  7   93203     7  2017 Gas- Commerc~ Y                     0        0         NA
##  8   93203     8  2017 Gas- Commerc~ Y                     0        0         NA
##  9   93203     9  2017 Gas- Commerc~ Y                     0        0         NA
## 10   93204     7  2017 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,481 more rows
## [1] "PGE_2018_Q3_ElectricUsageByZip.csv"
## [1] "PGE_2018_Q3_GasUsageByZip.csv"
## # A tibble: 3,503 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     7  2018 Gas- Commerc~ Y                     0        0         NA
##  2   92304     8  2018 Gas- Commerc~ Y                     0        0         NA
##  3   92304     9  2018 Gas- Commerc~ Y                     0        0         NA
##  4   92365     7  2018 Gas- Commerc~ Y                     0        0         NA
##  5   92365     8  2018 Gas- Commerc~ Y                     0        0         NA
##  6   92365     9  2018 Gas- Commerc~ Y                     0        0         NA
##  7   93203     7  2018 Gas- Commerc~ Y                     0        0         NA
##  8   93203     8  2018 Gas- Commerc~ Y                     0        0         NA
##  9   93203     9  2018 Gas- Commerc~ Y                     0        0         NA
## 10   93204     7  2018 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,493 more rows
## [1] "PGE_2019_Q3_ElectricUsageByZip.csv"
## [1] "PGE_2019_Q3_GasUsageByZip.csv"
## # A tibble: 3,489 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     7  2019 Gas- Commerc~ Y                     0        0         NA
##  2   92304     8  2019 Gas- Commerc~ Y                     0        0         NA
##  3   92304     9  2019 Gas- Commerc~ Y                     0        0         NA
##  4   92365     7  2019 Gas- Commerc~ Y                     0        0         NA
##  5   92365     8  2019 Gas- Commerc~ Y                     0        0         NA
##  6   92365     9  2019 Gas- Commerc~ Y                     0        0         NA
##  7   93203     7  2019 Gas- Commerc~ Y                     0        0         NA
##  8   93203     8  2019 Gas- Commerc~ Y                     0        0         NA
##  9   93203     9  2019 Gas- Commerc~ Y                     0        0         NA
## 10   93204     7  2019 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,479 more rows
## [1] "PGE_2017_Q4_ElectricUsageByZip.csv"
## [1] "PGE_2017_Q4_GasUsageByZip.csv"
## # A tibble: 4,662 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304     9  2017 Gas- Commerc~ Y                     0        0         NA
##  2   92304    10  2017 Gas- Commerc~ Y                     0        0         NA
##  3   92304    11  2017 Gas- Commerc~ Y                     0        0         NA
##  4   92304    12  2017 Gas- Commerc~ Y                     0        0         NA
##  5   92365     9  2017 Gas- Commerc~ Y                     0        0         NA
##  6   92365    10  2017 Gas- Commerc~ Y                     0        0         NA
##  7   92365    11  2017 Gas- Commerc~ Y                     0        0         NA
##  8   92365    12  2017 Gas- Commerc~ Y                     0        0         NA
##  9   93203     9  2017 Gas- Commerc~ Y                     0        0         NA
## 10   93203    10  2017 Gas- Commerc~ Y                     0        0         NA
## # ... with 4,652 more rows
## [1] "PGE_2018_Q4_ElectricUsageByZip.csv"
## [1] "PGE_2018_Q4_GasUsageByZip.csv"
## # A tibble: 3,496 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1      NA    NA    NA Gas-          <NA>                 NA       NA         NA
##  2   92304    10  2018 Gas- Commerc~ Y                     0        0         NA
##  3   92304    11  2018 Gas- Commerc~ Y                     0        0         NA
##  4   92304    12  2018 Gas- Commerc~ Y                     0        0         NA
##  5   92365    10  2018 Gas- Commerc~ Y                     0        0         NA
##  6   92365    11  2018 Gas- Commerc~ Y                     0        0         NA
##  7   92365    12  2018 Gas- Commerc~ Y                     0        0         NA
##  8   93203    10  2018 Gas- Commerc~ Y                     0        0         NA
##  9   93203    11  2018 Gas- Commerc~ Y                     0        0         NA
## 10   93203    12  2018 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,486 more rows
## [1] "PGE_2019_Q4_ElectricUsageByZip.csv"
## [1] "PGE_2019_Q4_GasUsageByZip.csv"
## # A tibble: 3,490 x 8
##    ZIPCODE MONTH  YEAR CUSTOMERCLASS COMBINED TOTALCUSTOMERS TOTALTHM AVERAGETHM
##      <dbl> <dbl> <dbl> <chr>         <chr>             <dbl>    <dbl>      <dbl>
##  1   92304    10  2019 Gas- Commerc~ Y                     0        0         NA
##  2   92304    11  2019 Gas- Commerc~ Y                     0        0         NA
##  3   92304    12  2019 Gas- Commerc~ Y                     0        0         NA
##  4   92365    10  2019 Gas- Commerc~ Y                     0        0         NA
##  5   92365    11  2019 Gas- Commerc~ Y                     0        0         NA
##  6   92365    12  2019 Gas- Commerc~ Y                     0        0         NA
##  7   93203    10  2019 Gas- Commerc~ Y                     0        0         NA
##  8   93203    11  2019 Gas- Commerc~ Y                     0        0         NA
##  9   93203    12  2019 Gas- Commerc~ Y                     0        0         NA
## 10   93204    10  2019 Gas- Commerc~ Y                     0        0         NA
## # ... with 3,480 more rows

Filtering to Zip Codes in the Bay Area

library(tidyverse)
library(sf)
library(tigris)
library(leaflet)
library (dplyr)


ca_counties <- counties("CA", cb = T, progress_bar = F)

st_crs(ca_counties)
## Coordinate Reference System:
##   User input: NAD83 
##   wkt:
## GEOGCRS["NAD83",
##     DATUM["North American Datum 1983",
##         ELLIPSOID["GRS 1980",6378137,298.257222101,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["latitude",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["longitude",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4269]]
projection <- "+proj=utm +zone=10 +ellps=GRS80 +datum=NAD83 +units=ft +no_defs"

ca_counties_transformed <- 
  ca_counties %>% 
  st_transform(4326) %>% 
  st_transform(26910) %>% 
  st_transform(projection) %>% 
  st_transform(st_crs(ca_counties))

usa_zips <- 
  zctas(cb = T, progress_bar = F)

bay_county_names <-
  c(
    "Alameda",
    "Contra Costa",
    "Marin",
    "Napa",
    "San Francisco",
    "San Mateo",
    "Santa Clara",
    "Solano",
    "Sonoma"
  )

bay_cbgs <- 
  bay_county_names %>% 
  map_dfr(function(county) {
    block_groups("CA", county, cb = T, progress_bar = F)
  })

bay_counties <-
  ca_counties %>%
  filter(NAME %in% bay_county_names)


bay_zips <-
  usa_zips %>% 
  st_centroid() %>% 
  .[bay_counties, ] %>% 
  st_set_geometry(NULL) %>% 
  left_join(usa_zips %>% select(GEOID10)) %>% 
  st_as_sf()

colnames(pge_elec_gas)
## [1] "ZIPCODE"        "MONTH"          "YEAR"           "CUSTOMERCLASS" 
## [5] "COMBINED"       "TOTALCUSTOMERS" "TOTALKBTU"
pge_rc_bayzips <-
  pge_elec_gas %>% 
  filter(CUSTOMERCLASS %in% c("Gas- Residential", "Elec- Residential", "Gas- Commercial", "Elec- Commercial")) %>% 
  mutate(
    ZIPCODE = ZIPCODE %>% as.character()
  ) %>% 
  group_by(ZIPCODE, CUSTOMERCLASS) %>%
  right_join(
    bay_zips %>% select(GEOID10),
    by = c("ZIPCODE" = "GEOID10")
  ) %>%  
  ungroup() %>% 
  group_by(CUSTOMERCLASS,MONTH,YEAR) %>% 
  summarize(
    TOTALKBTU = sum(TOTALKBTU, na.rm = T)
  ) 


  
class(pge_rc_bayzips$ZIPCODE)
## [1] "NULL"

Creating PGE Dataframe w/ Dates:

library(date)
library(zoo)
library(dplyr)

pge_rc_bayzips$Date <- as.yearmon(paste(pge_rc_bayzips$YEAR, pge_rc_bayzips$MONTH), "%Y %m")

pge_bayzips_date <- pge_rc_bayzips

Histogram - 9 Bay Area Counties Monthly Electric & Gas Usage, 2017-2020

library(tidyverse)
library(plotly)
library(zoo)


pge_bayzips_date <- transform(pge_bayzips_date, Date = as.Date(Date, frac = 1))
 
class(pge_bayzips_date$Date)
## [1] "Date"
pge_bay_chart <-
  pge_bayzips_date %>% 
  ggplot() +
  geom_bar(
    aes(
      x = Date,
      y = TOTALKBTU,
      fill = CUSTOMERCLASS
    ),
    stat = "identity",
    position = "stack"
  ) +
  labs(
    x = "Month",
    y = "KBTU",
    title = "9 Bay Area Counties Monthly Electric & Gas Usage, 2017-2020",
    fill = "Energy Type"
  ) 

pge_bay_chart %>% 
  ggplotly() %>% 
  layout(
    xaxis = list(fixedrange = T),
    yaxis = list(fixedrange = T)
  ) %>% 
  config(displayModeBar = F) 
pge_bay_chart

When comparing March of 2019 with March of 2020, there was a notable increase in residential electricity usage, which can likely be attributed an influx of people quarantining due to COVID-19. This increase in residential electricity usage continued from March through June.

Interestingly, residential gas Usage decreased slightly when comparing March 2019 to March 2020. However, residential gas Usage increased significantly in April 2020 as compared to April 2019, which could be attributed, again, to the influx of people spending more time at home.

As expected, commercial electricity usage was lesser for the first six months of 2020 overall than it was for the first six months of 2019. This is an unsurprising finding, since Bay Area “stay-at-home” orders would likely decrease the need for commercial electricity. Conversely, commercial gas usage experienced a slight increase during March 2020 and April 2020 compared to the same months in 2019, but then experienced a stark decrease moving into the summer months.

Note that both commercial and residential gas and electricity usage was abnormally large for the month of September in 2017. In the 2017 datasets, the PG&E data double counted energy use in the month of September for both Quarter 3 and Quarter 4. This mistake was not replicated in any of the other datasets for years 2018-2020, which is why September energy usage is much lower for those months.

Creating Covid Comparison Data:

library(tidyverse)
library(dplyr)
library(sf)
library(tigris)
library(leaflet)

pge_map_zips <-
  pge_elec_gas %>% 
  filter(CUSTOMERCLASS %in% c("Gas- Residential", "Elec- Residential", "Gas- Commercial", "Elec- Commercial")) %>% 
  mutate(
    ZIPCODE = ZIPCODE %>% as.character()
  ) %>% 
  group_by(ZIPCODE, YEAR, CUSTOMERCLASS, MONTH) %>%
  summarize(
    TOTALKBTU = sum(TOTALKBTU, na.rm = T)
  ) %>% 
  right_join(
    bay_zips %>% select(GEOID10),
    by = c("ZIPCODE" = "GEOID10")
  )

library(zoo)
pge_map_zips$Date <- as.yearmon(paste(pge_map_zips$YEAR, pge_map_zips$MONTH), "%Y %m")

pge_map_zips$Date <- NULL  


pge_covid <-
  pge_map_zips %>% 
  filter(CUSTOMERCLASS == "Elec- Residential", YEAR %in% 2019:2020, MONTH %in% 3:6) %>%
  pivot_wider(
    names_from = YEAR, 
    values_from = TOTALKBTU,
    values_fill = 0
  ) %>% 
  rename(
    KBTU2019 = "2019",
    KBTU2020 = "2020"
  ) %>%
  mutate(
    KBTU_change = ((KBTU2020 - KBTU2019)/(KBTU2019)) * 100
 ) %>% 
  na.omit() %>% 
 st_as_sf()

Map - Percentage Change in Residential Electricity Use During COVID-19

library(tidyverse)
library(sf)
library(tigris)
library(leaflet)


  res_pal <- colorNumeric(
  palette = "Blues",
  domain = 
    pge_covid$KBTU_change
)

leaflet() %>% 
  addTiles() %>% 
  addPolygons(
    data = pge_covid,
    fillColor = ~res_pal(KBTU_change),
    color = "white",
    opacity = 0.5,
    fillOpacity = 0.5,
    weight = 1,
    label = ~paste0(
      round(KBTU_change), 
      "KBTU Percentage Change in",
      ZIPCODE
    ),
    highlightOptions = highlightOptions(
      weight = 2,
      opacity = 1
    )
  ) %>% 
  addLegend(
    data = pge_covid,
    pal = res_pal,
    values = ~KBTU_change,
    title = "Percentage Change in Residential Electric Energy Use<br>During COVID-19"
  )

Aside from zipcodes belonging to Sonoma County, all of the zipcodes in the Bay Area experienced a positive percentage change in Residential Electricity Use during Covid-19. San Mateo, Santa Clara, Palo Alto, and Loma Mar did not experience a percentage change in Residential Electricity Use at all, though Stanford experiences a 8% change in Residential Electricity Use during COVID-19. A key assumption that is relevant in this analysis is the fact that these changes in Electricity occured during a state mandated lock-down that necessitated people remain home.